This calculator evaluates model weight storage footprints across quantization precision levels, including FP32, FP16/BF16, FP8/INT8, and INT4/FP4 formats. It calculates raw disk storage in decimal gigabytes (GB) and binary gibibytes (GiB) while reporting compression ratios relative to a selected baseline. Infrastructure engineers use it to plan storage optimization.
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Quantizing machine learning models reduces weight parameter precision, allowing massive foundation models to fit on smaller storage volumes and GPU memory configurations.
How to use it
Enter your target model’s total parameter count in billions of parameters. Select a baseline precision format to compute relative compression ratios.
The baseline selector defaults to FP16 / BF16 (16-bit), representing standard un-quantized open-weight releases.
Compare INT4 precision against an FP16 baseline to evaluate weight compression ratios achieved when quantizing local models.
Review the precision comparison table detailing storage sizes in decimal gigabytes (GB), binary gibibytes (GiB), and relative compression multiples against your baseline choice across all precision levels.
Fields explained
Parameters (billions) – total parameter count of the target model in billions. Default value is 7.0, step size 0.1.
Compare against – reference precision format used to calculate relative compression ratios. Options: FP32 (32-bit), FP16 / BF16 (16-bit), FP8 / INT8 (8-bit), INT4 (4-bit), FP4 / NF4 (4-bit). Default selection is FP16 / BF16 (16-bit).
Reading the results
| Precision Level | Bit Depth & Byte Factor | 7B Model Size (GB / GiB) | vs. FP16 Baseline |
|---|---|---|---|
| FP32 (32-bit) | 32 bits (4.0 bytes/param) | 28.00 GB (26.08 GiB) | 0.50× (2× larger than FP16) |
| FP16 / BF16 (16-bit) | 16 bits (2.0 bytes/param) | 14.00 GB (13.04 GiB) | 1.00× (Baseline) |
| FP8 / INT8 (8-bit) | 8 bits (1.0 byte/param) | 7.00 GB (6.52 GiB) | 2.00× compression |
| INT4 / FP4 (4-bit) | 4 bits (0.5 bytes/param) | 3.50 GB (3.26 GiB) | 4.00× compression |
Quantization compresses raw parameter weights linearly with bit depth. Transitioning from FP16 down to INT4 reduces static weight storage footprints fourfold.
Quantization calculations cover raw model weight storage only; actual runtime VRAM requirements include additional activation tensors and KV-cache allocations.
Evaluating weight compression ratios helps select efficient hosting hardware. Quantizing a 7B model from FP16 to INT4 drops weight size from 14.00 GB down to 3.50 GB.
The formula
Weight storage in decimal gigabytes multiplies parameters in billions by bytes per parameter (bits / 8). Binary gibibytes divide decimal byte volume by 1,024 cubed (1024³). Compression ratio divides baseline bit depth by the target format’s bit depth.
The mathematical representation for weight storage and compression ratios is:
BytesPerParam = Bits / 8
SizeGB = ParametersInBillions × BytesPerParam
SizeGiB = (SizeGB × 1,000,000,000) / (1024 × 1024 × 1024)
CompressionRatio = BaselineBits / TargetBits
| Quantization Format | Bit Precision | Primary Use Case |
|---|---|---|
| FP16 / BF16 | 16-bit | Standard un-quantized model training and high-precision reference evaluation |
| FP8 / INT8 | 8-bit | High-throughput cloud server inference with minimal quality loss |
| INT4 / NF4 | 4-bit | Local workstation inference, edge devices, and QLoRA fine-tuning |
Actual quantized file sizes on disk vary slightly from pure mathematical calculations due to un-quantized layer normalization tensors and quantization scale metadata.
For a baseline setup with a 7B parameter model compared against FP16 baseline: FP32 weight size equals 7 × 4 = 28.00 GB (0.50× ratio vs FP16). FP16 baseline equals 7 × 2 = 14.00 GB (1.00× ratio). INT8 weight size equals 7 × 1 = 7.00 GB (2.00× ratio). INT4 weight size equals 7 × 0.5 = 3.50 GB (4.00× ratio).
Worked examples
Compressing a 70B Foundation Model
An infrastructure team evaluates storage for a 70B model compared against FP16. Parameters: 70B, baseline FP16. FP16 baseline size: 70 × 2 = 140.00 GB (130.39 GiB). INT8 size: 70 × 1 = 70.00 GB (65.19 GiB, 2.00× ratio). INT4 size: 70 × 0.5 = 35.00 GB (32.60 GiB, 4.00× ratio). Quantizing a 70B model to INT4 cuts weight storage from 140.00 GB down to 35.00 GB (4.00× compression). The team selects INT4 to host 70B models on single 48GB GPU cards.
Deploying a 13B Model on 8GB Consumer GPUs
A developer checks if a 13B model can fit on an 8GB GPU card. Parameters: 13B, baseline FP16. FP16 size: 13 × 2 = 26.00 GB (exceeds 8GB). INT8 size: 13 × 1 = 13.00 GB (exceeds 8GB). INT4 size: 13 × 0.5 = 6.50 GB (6.05 GiB, 4.00× ratio). INT4 quantization compresses the 13B model weights down to 6.50 GB, enabling execution on 8GB graphics cards.
Evaluating FP32 Base Workstation Models
An engineer compares a 3B model against FP32 baseline precision. Parameters: 3B, baseline FP32. FP32 baseline size: 3 × 4 = 12.00 GB. FP16 size: 3 × 2 = 6.00 GB (2.00× ratio). INT8 size: 3 × 1 = 3.00 GB (4.00× ratio). INT4 size: 3 × 0.5 = 1.50 GB (8.00× ratio). Converting from legacy FP32 down to INT4 achieves an 8.00× compression ratio.
Sizing a 34B Model Cluster
An enterprise sizes disk arrays for a 34B parameter model. Parameters: 34B, baseline FP16. FP16 size: 34 × 2 = 68.00 GB (63.33 GiB). INT8 size: 34 × 1 = 34.00 GB (31.66 GiB). INT4 size: 34 × 0.5 = 17.00 GB (15.83 GiB). INT4 quantization reduces storage by 51.00 GB per node instance.
Common mistakes
Confusing weight file size on disk with total VRAM required during inference runtime is a major error. Weight quantization compresses static model files on disk, but runtime VRAM must also account for activation tensors and KV-cache allocations.
Assuming 4-bit quantization degrades model intelligence severely is incorrect. Modern post-training quantization methods (such as AWQ, GPTQ, or NF4) preserve high task accuracy and perplexity scores down to 4-bit precision.
Ignoring binary GiB vs. decimal GB conversion causes storage planning errors. A 35 GB model file occupies ~32.6 GiB on Linux and Windows file systems, which report drive capacity in binary GiB.
Assuming raw weight size equals total inference VRAM leads to out-of-memory crashes when context windows fill up under load.
Use AWQ or GPTQ quantization frameworks to maintain accuracy while achieving 4-bit compression ratios.
FAQ
What is model quantization in deep learning?
Quantization compresses model weight tensors by mapping high-precision floating-point numbers (like 16-bit FP16) to lower-bit representations (like 8-bit INT8 or 4-bit INT4).
Quantization reduces weight storage space and speeds up memory-bandwidth-bound inference decoding.
How much quality loss occurs when quantizing from FP16 to INT4?
Modern quantization algorithms (like AWQ, GPTQ, or QLoRA NF4) achieve 4-bit weight compression with minimal degradation in perplexity or task accuracy for models exceeding 7B parameters.
Models under 3B parameters experience slightly higher quality drop-offs when quantized to 4-bit.
What is the difference between GPTQ, AWQ, and GGUF quantization?
GPTQ and AWQ are GPU-optimized 4-bit quantization formats designed for fast matrix multiplication on NVIDIA hardware. GGUF is a CPU/metal-optimized quantization format used by llama.cpp for local workstation execution.
Select format architectures based on your target execution hardware (GPU vs. CPU).
Why is FP4 / NF4 popular for fine-tuning?
NormalFloat4 (NF4) is an information-theoretically optimal 4-bit data type introduced in QLoRA fine-tuning. It distributes weight parameters evenly across 4-bit bins, preserving pre-training knowledge better than standard INT4 formats.
NF4 enables high-quality fine-tuning of large models on consumer GPU hardware.
Does quantization speed up model generation?
Yes. Autoregressive LLM generation is memory-bandwidth bound. Smaller 4-bit model weights require fewer bytes to be transferred from VRAM to compute cores for every token, speeding up generation rates.
Quantization improves both storage footprint and generation speed on memory-bound hardware.
Disclaimer
This calculator provides weights-only disk storage and compression ratio estimates based on mathematical formulas for parameter bit precision. Actual quantized file sizes vary slightly due to un-quantized layer norm tensors, scale metadata overhead, quantization format structures, and operating system file allocation rules.
The interactive calculator on this page serves as the primary resource for testing quantization storage scenarios. Infrastructure engineers should verify actual checkpoint file sizes on target hosting volumes before finalizing storage drive provisioning allocations.
This calculator is exactly what I’ve been looking for when evaluating local model deployments. Running a 70B Llama 3 model on my Mac Studio with M3 Max was impossible until I understood the actual VRAM footprint. With FP16, you’re looking at 140GB of weight storage alone, but quantizing down to INT4 cuts that to 35GB, which finally fits in unified memory without constant swapping. I’ve been using llama.cpp with GGUF quantization, and those compression ratios match what you’re showing here pretty closely. The calculator’s distinction between decimal GB and binary GiB matters more than people realize when you’re actually allocating memory buffers in PyTorch or running inference through Ollama. What I really appreciate is that this doesn’t pretend quantization is free. You still lose precision, and for some tasks like instruction-following with Mistral 7B, INT4 introduces noticeable quality degradation. But for retrieval-augmented generation or simple classification tasks running locally, the storage savings justify the tradeoff. The privacy angle can’t be overstated either—keeping models and data completely on-device, no API calls to OpenAI or Anthropic, means your documents never leave your hardware. This calculator should be required reading for anyone considering Hugging Face model downloads.
Regarding the M3 Max deployment specifics you mentioned, your point about GGUF quantization matching these theoretical calculations is valuable. The reason actual disk sizes vary slightly from pure math (as we note in the article) is exactly because of those layer normalization tensors and quantization scales you’re contending with in llama.cpp. One thing worth adding: the unified memory architecture on Apple Silicon handles INT4 models differently than discrete VRAM on NVIDIA GPUs. The lack of VRAM bottlenecking on Mac means you can often use slightly higher precision (INT8 instead of INT4) and still get acceptable performance, which the calculator doesn’t account for since it assumes traditional GPU constraints. If you’re running through Ollama, you might find experimenting with the quantization format (NF4 vs INT4) yields better quality/speed tradeoffs on your specific workload. The privacy preservation point is crucial—people often overlook that hosting via API involves token-level surveillance in ways local inference doesn’t.
INT4 compression doesn’t come close to what you get with proper LoRA fine-tuning. Storing 70B at 3.5 bits per param is clever, but the quality drop is real compared to keeping full precision. vLLM handles batch inference way better than local quantized setups anyway.
Your mention of vLLM is apt for inference serving, though the contexts are slightly different. vLLM excels at batched production inference on server hardware (H100s, A100s), where you want FP16 or INT8 to maintain quality across many concurrent requests. The quantization calculator here targets local/edge deployment, where INT4 is often the only viable option to fit models on consumer hardware at all. Regarding LoRA fine-tuning, you’re touching on an important distinction: QLoRA uses INT4 base models precisely to enable parameter-efficient fine-tuning on limited VRAM, which is a different optimization problem than static inference. The quality gap between INT4 and FP16 does vary significantly by task—we’ve seen it matter most on reasoning-heavy benchmarks like MMLU, but less so on classification. What specific task led you to prefer higher precision despite the storage overhead?
Fair point on the different use cases. I’ve mostly been working with production inference pipelines, so vLLM is my daily driver. But you’re right that QLoRA with INT4 makes sense for fine-tuning on limited hardware. I was thinking mainly about response quality on open-ended generation, where INT4 definitely shows more hallucinations compared to INT8 in my testing.
That’s a concrete observation about hallucination rates in open-ended generation. The trade-off between INT8 and INT4 often comes down to whether you’re willing to accept that degradation for the 2x storage reduction. Some teams mitigate this by using INT4 for the majority of layers but keeping embedding and output layers at INT8, though that’s more manual tuning than the calculator addresses. Since you’re running production pipelines, have you experimented with mixed-precision quantization strategies, or do you stick with uniform precision across the model?